# https://www.philschmid.de/fine-tune-llms-in-2024-with-trl#3-create-and-prepare-the-dataset
import gradio as gr
import os, torch
from datasets import load_dataset
from huggingface_hub import HfApi, login
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments

hf_profile = "bstraehle"

action_1 = "Fine-tune pre-trained model"
action_2 = "Prompt fine-tuned model"

system_prompt = "You are a text to SQL query translator. Given a question in English, generate a SQL query based on the provided SCHEMA. Do not generate any additional text. SCHEMA: {schema}"
user_prompt = "What is the total trade value and average price for each trader and stock in the trade_history table?"
schema = "CREATE TABLE trade_history (id INT, trader_id INT, stock VARCHAR(255), price DECIMAL(5,2), quantity INT, trade_time TIMESTAMP);"

base_model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"
dataset = "b-mc2/sql-create-context"

def process(action, base_model_id, dataset, system_prompt, user_prompt, schema):
    #raise gr.Error("Please clone and bring your own credentials.")
    if action == action_1:
        result = fine_tune_model(base_model_id, dataset)
    elif action == action_2:
        fine_tuned_model_id = replace_hf_profile(base_model_id)
        result = prompt_model(fine_tuned_model_id, system_prompt, user_prompt, schema)
    return result

# Preprocess the dataset
def preprocess(examples):
    model_inputs = tokenizer(examples["text"], text_target=examples["sql"], max_length=512, truncation=True)
    return model_inputs

def fine_tune_model(base_model_id, dataset):
#    tokenizer = download_model(base_model_id)
#    fine_tuned_model_id = upload_model(base_model_id, tokenizer)
#    return fine_tuned_model_id
    # Load the dataset
    dataset = load_dataset("gretelai/synthetic_text_to_sql")
    
    # Load pre-trained model and tokenizer
    model_name = "meta-llama/Meta-Llama-3.1-8B-Instruct"
    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
    tokenizer = AutoTokenizer.from_pretrained(model_name)
        
    dataset = dataset.map(preprocess, batched=True)
    
    # Split dataset to training and validation sets
    train_dataset = dataset["train"].shuffle(seed=42).select(range(1000))  # Adjust the range as needed
    val_dataset = dataset["test"].shuffle(seed=42).select(range(100))  # Adjust the range as needed
    
    # Set training arguments
    training_args = Seq2SeqTrainingArguments(
        output_dir="./results",
        num_train_epochs=3,  # Adjust as needed
        per_device_train_batch_size=16,
        per_device_eval_batch_size=64,
        warmup_steps=500,
        weight_decay=0.01,
        logging_dir="./logs",
        save_total_limit=2,
        save_steps=500,
        eval_steps=500,
        metric_for_best_model="accuracy",
        greater_is_better=True,
        save_on_each_node=True,
        load_best_model_at_end=True,
    )
    
    # Create Trainer instance
    trainer = Seq2SeqTrainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=val_dataset,
        compute_metrics=lambda pred: {"accuracy": torch.sum(pred.label_ids == pred.predictions.argmax(-1))},
    )
    
    # Train the model
    trainer.train()

    # Save the trained model
    trainer.save_model("./fine_tuned_model")

    # Create a repository object
    repo = Repository(
        local_dir="./fine_tuned_model",
        repo_type="model",
        repo_id="bstraehle/Meta-Llama-3.1-8B-Instruct-text-to-sql",
    )
    
    # Login to the Hugging Face hub
    repo.login(token=os.environ["HF_TOKEN"])
    
    # Push the model to the hub
    repo.push_to_hub(commit_message="Initial commit")

def prompt_model(model_id, system_prompt, user_prompt, schema):
    pipe = pipeline("text-generation", 
                    model=model_id, 
                    model_kwargs={"torch_dtype": torch.bfloat16}, 
                    device_map="auto",
                    max_new_tokens=1000)
    messages = [
      {"role": "system", "content": system_prompt.format(schema=schema)},
      {"role": "user", "content": user_prompt},
      {"role": "assistant", "content": ""}
    ]
    output = pipe(messages)
    result = output[0]["generated_text"][-1]["content"]
    print(result)
    return result

def download_model(base_model_id):
    tokenizer = AutoTokenizer.from_pretrained(base_model_id)    
    model = AutoModelForCausalLM.from_pretrained(base_model_id)    
    model.save_pretrained(base_model_id)
    return tokenizer
    
def upload_model(base_model_id, tokenizer):
    fine_tuned_model_id = replace_hf_profile(base_model_id)
    login(token=os.environ["HF_TOKEN"])
    api = HfApi()
    #api.delete_repo(repo_id=fine_tuned_model_id, repo_type="model")
    api.create_repo(repo_id=fine_tuned_model_id)
    api.upload_folder(
        folder_path=base_model_id,
        repo_id=fine_tuned_model_id
    )
    tokenizer.push_to_hub(fine_tuned_model_id)
    return fine_tuned_model_id

def replace_hf_profile(base_model_id):
    model_id = base_model_id[base_model_id.rfind('/')+1:]
    return f"{hf_profile}/{model_id}"

demo = gr.Interface(fn=process, 
                    inputs=[gr.Radio([action_1, action_2], label = "Action", value = action_1),
                            gr.Textbox(label = "Base Model ID", value = base_model_id, lines = 1),
                            gr.Textbox(label = "Dataset", value = dataset, lines = 1),
                            gr.Textbox(label = "System Prompt", value = system_prompt, lines = 2),
                            gr.Textbox(label = "User Prompt", value = user_prompt, lines = 2),
                            gr.Textbox(label = "Schema", value = schema, lines = 2)],
                    outputs=[gr.Textbox(label = "Completion", value = os.environ["OUTPUT"])])
demo.launch()